STATISTICAL SENSITIVITY MEASURE OF SINGLE LAYER PERCEPTRON NEURAL NETWORKS TO INPUT PERTURBATION

摘要：

In this work, we study the statistical output sensitivity measure of a trained single layer preceptron neural network to input perturbation. This quantitative measure computes the expectation of absolute output deviations due to input perturbation with respect to all possible inputs. This is an important first step to the study of the statistical output sensitivity measure of multilayer perceptron neural networks.The major contribution of this work is the relaxation of the restriction of the input having uniform distributions in our early studies. Therefore, the novel sensitivity measure is applicable to real world applications such as machine learning problems. Furthermore, experimental results show that the new sensitivity measure is suitable to the networks with large input dimension.

关键词： Sensitivity Analysis Single Layer Perceptron Neural Networks

作者: XIAO-QIN ZENG WING W.Y.NG DANIEL S.YEUNG

作者单位: Department of Computer Science and Engineering, Hohai University, Nanjing 210098, China;Media and Li Media and Life Science Computing Laboratory, Shenzhen Graduate School, Harbin Institute of Technolog

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3101-3105

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）